An Efficient Algorithm for Mining Maximal Frequent Sequential Patterns in Large Databases

被引：0

作者：

Su, Qiu-bin ^{[1
]}

Lu, Lu ^{[1
,2
]}

Cheng, Bin ^{[1
]}

机构：

[1] South China Univ Technol, Sch Comp Sci & Engn, Guangzhou, Guangdong, Peoples R China

[2] South China Univ Technol, Modern Ind Technol Res Inst, Zhongshan, Peoples R China

来源：

2018 INTERNATIONAL CONFERENCE ON COMMUNICATION, NETWORK AND ARTIFICIAL INTELLIGENCE (CNAI 2018) | 2018年

关键词：

Sequential pattern mining; Frequent sequence mining; Pruning strategy; data stream;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Frequent sequence mining is one of the important research directions of click stream analysis, this paper studies the problem of mining maximal frequent sequences in mobile app clickstreams. Different from frequent itemsets mining, frequent sequence mining takes the time order of elements into account. In this paper, MFSGrowth(Maximal Frequent Sequence Growth) is proposed for fast discovery of frequent sequence. MFSGrowth is an efficient algorithm based on the FP Tree, combined with the storage characteristics of the TriedTree, experiments show that the algorithm performs well in both mining time and storage efficiency.

引用

页码：404 / 410

页数：7

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